



'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor







class 			__magic_name__		(					unittest.TestCase     ):







							def __init__(							self      : List[str]			,_UpperCAmelCase      : Dict			,_UpperCAmelCase      : List[Any]=7			,_UpperCAmelCase      : Optional[int]=3			,_UpperCAmelCase      : int=18			,_UpperCAmelCase      : List[Any]=30			,_UpperCAmelCase      : str=400			,_UpperCAmelCase      : List[str]=True			,_UpperCAmelCase      : int=None			,_UpperCAmelCase      : int=0.8_75			,_UpperCAmelCase      : Optional[Any]=True			,_UpperCAmelCase      : int=[0.5, 0.5, 0.5]			,_UpperCAmelCase      : Optional[int]=[0.5, 0.5, 0.5]			,):
									_a  :   Optional[int]				 =							size if size is not None else {'shortest_edge': 20}
									_a  :   int				 =							parent
									_a  :   Optional[int]				 =							batch_size
									_a  :   List[str]				 =							num_channels
									_a  :   Any				 =							image_size
									_a  :   Dict				 =							min_resolution
									_a  :   Tuple				 =							max_resolution
									_a  :   Optional[int]				 =							do_resize
									_a  :   str				 =							size
									_a  :   List[str]				 =							crop_pct
									_a  :   str				 =							do_normalize
									_a  :   Union[str, Any]				 =							image_mean
									_a  :   List[str]				 =							image_std







							def 							__lowercase    (							self      : Union[str, Any]	):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_pct": self.crop_pct,
									}







@require_torch
@require_vision
class 			__magic_name__		(					lowerCAmelCase_    ,		unittest.TestCase     ):
							lowerCAmelCase :					str            =      ConvNextImageProcessor if is_vision_available() else None







							def 							__lowercase    (							self      : Any	):
									_a  :   Any				 =							ConvNextImageProcessingTester(self	)







							@property
							def 							__lowercase    (							self      : Optional[int]	):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 							__lowercase    (							self      : str	):
									_a  :   Tuple				 =							self.image_processing_class(**self.image_processor_dict	)
									self.assertTrue(hasattr(lowerCamelCase_			,'do_resize'	)	)
									self.assertTrue(hasattr(lowerCamelCase_			,'size'	)	)
									self.assertTrue(hasattr(lowerCamelCase_			,'crop_pct'	)	)
									self.assertTrue(hasattr(lowerCamelCase_			,'do_normalize'	)	)
									self.assertTrue(hasattr(lowerCamelCase_			,'image_mean'	)	)
									self.assertTrue(hasattr(lowerCamelCase_			,'image_std'	)	)







							def 							__lowercase    (							self      : List[str]	):
									_a  :   List[Any]				 =							self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size			,{'shortest_edge': 20}	)

									_a  :   Union[str, Any]				 =							self.image_processing_class.from_dict(self.image_processor_dict			,size=42	)
									self.assertEqual(image_processor.size			,{'shortest_edge': 42}	)







							def 							__lowercase    (							self      : Tuple	):
									pass







							def 							__lowercase    (							self      : str	):
									_a  :   int				 =							self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									_a  :   List[str]				 =							prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase_	)
									for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_			,Image.Image	)

									# Test not batched input
									_a  :   Any				 =							image_processing(image_inputs[0]			,return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape			,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['shortest_edge'],
									        self.image_processor_tester.size['shortest_edge'],
									    )			,)

									# Test batched
									_a  :   int				 =							image_processing(lowerCamelCase_			,return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape			,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['shortest_edge'],
									        self.image_processor_tester.size['shortest_edge'],
									    )			,)







							def 							__lowercase    (							self      : Any	):
									_a  :   int				 =							self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									_a  :   Tuple				 =							prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase_			,numpify=lowerCamelCase_	)
									for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_			,np.ndarray	)

									# Test not batched input
									_a  :   str				 =							image_processing(image_inputs[0]			,return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape			,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['shortest_edge'],
									        self.image_processor_tester.size['shortest_edge'],
									    )			,)

									# Test batched
									_a  :   Optional[Any]				 =							image_processing(lowerCamelCase_			,return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape			,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['shortest_edge'],
									        self.image_processor_tester.size['shortest_edge'],
									    )			,)







							def 							__lowercase    (							self      : Any	):
									_a  :   Dict				 =							self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									_a  :   Any				 =							prepare_image_inputs(self.image_processor_tester			,equal_resolution=lowerCamelCase_			,torchify=lowerCamelCase_	)
									for image in image_inputs:
											self.assertIsInstance(lowerCamelCase_			,torch.Tensor	)

									# Test not batched input
									_a  :   Union[str, Any]				 =							image_processing(image_inputs[0]			,return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape			,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['shortest_edge'],
									        self.image_processor_tester.size['shortest_edge'],
									    )			,)

									# Test batched
									_a  :   int				 =							image_processing(lowerCamelCase_			,return_tensors='pt'	).pixel_values
									self.assertEqual(
									    encoded_images.shape			,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['shortest_edge'],
									        self.image_processor_tester.size['shortest_edge'],
									    )			,)









'''simple docstring'''

from __future__ import annotations

# Define the Gravitational Constant G and the function
__lowerCAmelCase		    =     6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   dict[str, float]:
		_a  :   str				 =							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:
				_a  :   List[Any]				 =							GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
				return {"force": force}
		elif mass_a == 0:
				_a  :   Any				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_1": mass_a}
		elif mass_a == 0:
				_a  :   Tuple				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_2": mass_a}
		elif distance == 0:
				_a  :   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()




